Fast content-aware resizing of multi-layer information visualization via adaptive triangulation. (April 2018)

• Record Type: Journal Article
• Authors: Li, Chenhui; Baciu, George; Wang, Yunzhe; Zhang, Xiujun
• Is Part Of: Journal of visual languages & computing. Volume 45(2018)
• Page Start: 61
• Page End: 73
• Publication Date: 2018-04
• Subjects: Content-aware resizing -- Information visualization -- Triangulation -- Saliency map -- Context preserving -- Focus+context; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.03.004
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Visual graphics and image-based content have become the pervasive modes of interaction with the digital information flow. With the immense proliferation of display systems and devices, visual content representation has become increasingly challenging. Classical static image resizing algorithms are not directly suitable for the current dynamic information visualization of streaming data flows and processes because most of the visual content often consists of superimposed, multi-layered, multi-scale structure. In this paper, we propose a new adaptive method for content-aware resizing of visual information flow. Scaling is performed by deforming a hierarchical triangle mesh that matches the visual saliency map (VSM) of the streaming data. The VSM is generated automatically based on a series of predefined rules operating on a triangular mesh representation of visual features. We present a linear energy function to minimize distortions of the triangular deformations to perceptually preserve informative content. Through multiple experiments on real datasets, we show that the method has both high performance as well as high robustness in the presence of large differences in the visual aspect ratios between target displays.